{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:10:29Z","timestamp":1790266229620,"version":"4.1.0"},"reference-count":73,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry, and Energy","doi-asserted-by":"crossref","award":["20026184"],"award-info":[{"award-number":["20026184"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To achieve Level 4 and above autonomous driving, vehicle localization must remain accurate and reliable under diverse real-world conditions, including complex traffic scenarios, environmental changes, and partial sensor failures. Conventional localization approaches primarily rely on geometric consistency among multi-sensor observations, which can produce physically or contextually implausible pose estimates when sensor reliability degrades or observations become ambiguous. This paper proposes a semantic localization framework that integrates ontology-based semantic reasoning directly into the localization process. The proposed approach reformulates localization as a context-aware constraint selection problem guided by semantic consistency among objects, places, and vehicle poses. By evaluating logical and contextual validity at the hypothesis generation stage, semantically invalid pose hypotheses are eliminated early, and only situation-appropriate semantic constraints are selectively applied during optimization. As a result, compared to the localization system without semantic rules, the proposed framework achieves an average reduction of approximately 35.6% in mean localization error and 47.0% in maximum localization error across both longitudinal and lateral directions. Specifically, the framework supports structured multi-sensor fusion by selectively using sensor information semantically relevant to the driving context. Through this semantics-driven hypothesis reduction, the system reduces computational complexity while enhancing localization robustness and accuracy, particularly under sensor degradation and dynamic environmental conditions. The design of the semantic reasoning structure is also adaptable to cooperative perception scenarios, such as V2V-based information sharing.<\/jats:p>","DOI":"10.3390\/s26041328","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:51:09Z","timestamp":1771491069000},"page":"1328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Context-Aware Semantic Localization with Adaptive Sensor Fusion Under Adverse Conditions"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-4865","authenticated-orcid":false,"given":"Jun-Hyeon","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4454-129X","authenticated-orcid":false,"given":"Dong-Su","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2842-3841","authenticated-orcid":false,"given":"Ye-Chan","family":"An","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5506-3136","authenticated-orcid":false,"given":"Tae-Wook","family":"Eum","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4618-542X","authenticated-orcid":false,"given":"Jin-Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gi-Hyeon","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robotics, College of Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-0088","authenticated-orcid":false,"given":"Tae-Yong","family":"Kuc","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1676-7575","authenticated-orcid":false,"given":"Jeong-Won","family":"Pyo","sequence":"additional","affiliation":[{"name":"DXR Co., Ltd., R&D Center, Seoul 01411, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Joo, S., Bae, S., Choi, J., Park, H., Lee, S., You, S., Uhm, T., Moon, J., and Kuc, T. (2022). A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments. Electronics, 11.","DOI":"10.3390\/electronics11152420"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bae, S., Joo, S., Choi, J., Pyo, J., Park, H., and Kuc, T. (2023). Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems. Electronics, 12.","DOI":"10.3390\/electronics12092131"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Choi, J.H., Seo, D.S., Bae, S.H., An, Y.C., Kim, E.J., Pyo, J.W., and Kuc, T.Y. (2025). A Semantic Energy-Aware Ontological Framework for Adaptive Task Planning and Allocation in Intelligent Mobile Systems. Electronics, 14.","DOI":"10.3390\/electronics14183647"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Choi, J.H., Bae, S.H., Gilberto, G.G., Seo, D.S., Kwon, S.W., Kwon, G.H., Ahn, Y.C., Joo, K.J., and Kuc, T.Y. (November, January 29). A Multi-robot Navigation Framework using Semantic Knowledge for Logistics Environment. Proceedings of the 2024 24th International Conference on Control, Automation and Systems (ICCAS), Jeju, Republic of Korea.","DOI":"10.23919\/ICCAS63016.2024.10773065"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khan, M.U., Zaidi, S.A.A., Ishtiaq, A., Bukhari, S.U.R., Samer, S., and Farman, A. (2021, January 15\u201317). A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies. Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), Karachi, Pakistan.","DOI":"10.1109\/MAJICC53071.2021.9526266"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s41074-017-0027-2","article-title":"Visual SLAM algorithms: A survey from 2010 to 2016","volume":"9","author":"Taketomi","year":"2017","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_7","unstructured":"Nister, D., Naroditsky, O., and Bergen, J. (July, January 27). Visual odometry. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR, Washington, DC, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018, January 1\u20135). LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hess, W., Kohler, D., Rapp, H., and Andor, D. (2016, January 16\u201321). Real-time loop closure in 2D LIDAR SLAM. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual\u2013Inertial, and Multimap SLAM","volume":"37","author":"Campos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/LRA.2021.3064227","article-title":"FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., and Rus, D. (2020\u201324, January 24). LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/LRA.2020.2965415","article-title":"DeepFactors: Real-Time Probabilistic Dense Monocular SLAM","volume":"5","author":"Czarnowski","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1109\/TRO.2022.3150683","article-title":"LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM","volume":"38","author":"Cattaneo","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/LRA.2024.3498779","article-title":"GenZ-ICP: Generalizable and Degeneracy-Robust LiDAR Odometry Using an Adaptive Weighting","volume":"10","author":"Lee","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Koide, K., Yokozuka, M., Oishi, S., and Banno, A. (June, January 30). Voxelized GICP for Fast and Accurate 3D Point Cloud Registration. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9560835"},{"key":"ref_18","first-page":"10421","article-title":"BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework","volume":"Volume 35","author":"Koyejo","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_19","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020). EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. European Conference on Computer Vision, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, P., Zhao, X., Zeng, L., Liu, L., Liu, S., Sun, L., Li, Z., Chen, H., Liu, G., and Qiao, Z. (2025). A Review of Research on SLAM Technology Based on the Fusion of LiDAR and Vision. Sensors, 25.","DOI":"10.3390\/s25051447"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., and Song, S. (2018, January 21\u201325). Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8461224"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Toroslu, I., and Do\u011fan, M. (2018, January 20\u201323). Effective sensor fusion of a mobile robot for SLAM implementation. Proceedings of the 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand.","DOI":"10.1109\/ICCAR.2018.8384648"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qian, H., Wang, M., Zhu, M., and Wang, H. (2025). A Review of Multi-Sensor Fusion in Autonomous Driving. Sensors, 25.","DOI":"10.3390\/s25196033"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yeong, D.J., Velasco-Hernandez, G., Barry, J., and Walsh, J. (2021). Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review. Sensors, 21.","DOI":"10.20944\/preprints202102.0459.v1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dhaigude, V., and Kulkarni, A. (2025, January 16\u201317). A Comprehensive Review of Sensor Fusion Techniques in SLAM: Trends, Challenges and Future Directions. Proceedings of the 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET), Pune, India.","DOI":"10.1109\/ICAET63349.2025.10932171"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2024.08.007","article-title":"Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces","volume":"217","author":"Lin","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fan, S., Chen, X., Zhang, W., Xu, P., Zuo, Z., Tan, X., He, X., Sheikder, C., Guo, M., and Li, C. (2025). HV-LIOM: Adaptive Hash-Voxel LiDAR\u2013Inertial SLAM with Multi-Resolution Relocalization and Reinforcement Learning for Autonomous Exploration. Sensors, 25.","DOI":"10.3390\/s25247558"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/ACCESS.2024.3523129","article-title":"Adaptive Multi-Sensor Fusion for SLAM: A Scan Context-Driven Approach","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Access"},{"key":"ref_29","unstructured":"Xu, J., Huang, G., Yu, W., Zhang, X., Zhao, L., Li, R., Yuan, S., and Xie, L. (2024). Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S1367-5788(02)00045-7","article-title":"Sensor fusion","volume":"26","author":"Sasiadek","year":"2002","journal-title":"Annu. Rev. Control"},{"key":"ref_31","unstructured":"Moore, T., and Stouch, D. (2015). A generalized extended kalman filter implementation for the robot operating system. Proceedings of the Intelligent Autonomous Systems 13: 13th International Conference IAS-13, Padua, Italy, 3 September 2015, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gao, X., Wang, Z., Feng, Y., Ma, L., Chen, Z., and Xu, B. (2023, January 3\u20139). Benchmarking robustness of ai-enabled multi-sensor fusion systems: Challenges and opportunities. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, San Francisco, CA, USA.","DOI":"10.1145\/3611643.3616278"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"39830","DOI":"10.1109\/ACCESS.2020.2975643","article-title":"A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods","volume":"8","author":"Alatise","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.robot.2014.12.006","article-title":"Semantic mapping for mobile robotics tasks: A survey","volume":"66","author":"Kostavelis","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8778","DOI":"10.1109\/LRA.2024.3451390","article-title":"NEDS-SLAM: A Neural Explicit Dense Semantic SLAM Framework Using 3D Gaussian Splatting","volume":"9","author":"Ji","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rosinol, A., Abate, M., Chang, Y., and Carlone, L. (August, January 31). Kimera: An Open-Source Library for Real-Time Metric-Semantic Localization and Mapping. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196885"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hughes, N., Chang, Y., and Carlone, L. (2022). Hydra: A Real-time Spatial Perception Engine for 3D Scene Graph Construction and Optimization. arXiv.","DOI":"10.15607\/RSS.2022.XVIII.050"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"22119","DOI":"10.1109\/JSEN.2023.3306371","article-title":"Visual SLAM Integration With Semantic Segmentation and Deep Learning: A Review","volume":"23","author":"Pu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7426","DOI":"10.1109\/TITS.2025.3556928","article-title":"Semantic Visual Simultaneous Localization and Mapping: A Survey","volume":"26","author":"Chen","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, N., Lu, H., Zheng, Z., Liu, Y.H., and Chen, X. (2025, January 19\u201325). Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM. Proceedings of the 2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China.","DOI":"10.1109\/IROS60139.2025.11246943"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1007\/s40747-024-01408-0","article-title":"Lp-slam: Language-perceptive RGB-D SLAM framework exploiting large language model","volume":"10","author":"Zhang","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_42","first-page":"1","article-title":"Global-Context-Aware Visual Odometry System With Epipolar-Geometry-Constrained Loss Function","volume":"73","author":"Ji","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_43","unstructured":"Tourani, A., Ejaz, S., Bavle, H., Fernandez-Cortizas, M., Morilla-Cabello, D., Sanchez-Lopez, J.L., and Voos, H. (2025). vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, B., Hao, V.C., Stuckey, P.J., Reid, I., and Rezatofighi, H. (2025). Hier-SLAM++: Neuro-Symbolic Semantic SLAM with a Hierarchically Categorical Gaussian Splatting. arXiv.","DOI":"10.1109\/ICRA55743.2025.11127775"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Maleti\u0107, M., Peti, M., Petrovi\u0107, T., and Bogdan, S. (2025, January 2\u20135). Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations. Proceedings of the 2025 European Conference on Mobile Robots (ECMR), Padova, Italy.","DOI":"10.1109\/ECMR65884.2025.11163229"},{"key":"ref_46","unstructured":"Li, L., Ye, Y., Zhou, Y., Jiang, B., and Zeng, W. (2025). GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model. arXiv."},{"key":"ref_47","first-page":"131","article-title":"Enhanced Natural Language Annotation and Query for Semantic Mapping in Visual SLAM Using Large Language Models","volume":"1","author":"Guo","year":"2025","journal-title":"J. Sustain. Policy Pract."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pyo, J.W., Choi, J.H., and Kuc, T.Y. (2024). An Object-Centric Hierarchical Pose Estimation Method Using Semantic High-Definition Maps for General Autonomous Driving. Sensors, 24.","DOI":"10.3390\/s24165191"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, X., Milioto, A., Palazzolo, E., Gigu\u00e8re, P., Behley, J., and Stachniss, C. (2019, January 3\u20138). SuMa++: Efficient LiDAR-based Semantic SLAM. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967704"},{"key":"ref_50","first-page":"1","article-title":"Measure for Semantics and Semantically Constrained Pose Optimization: A Review","volume":"73","author":"Xia","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Igelbrink, F., Renz, M., G\u00fcnther, M., Powell, P., Niecksch, L., Lima, O., Atzmueller, M., and Hertzberg, J. (2024). Online Knowledge Integration for 3D Semantic Mapping: A Survey. arXiv.","DOI":"10.2139\/ssrn.5079129"},{"key":"ref_52","first-page":"1","article-title":"A Survey of Visual SLAM in Dynamic Environment: The Evolution From Geometric to Semantic Approaches","volume":"73","author":"Wang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Miao, L., Liu, W., and Deng, Z. (2025). A Frontier Review of Semantic SLAM Technologies Applied to the Open World. Sensors, 25.","DOI":"10.3390\/s25164994"},{"key":"ref_54","unstructured":"Zeng, F., Gan, W., Huai, Z., Sun, L., Chen, H., Wang, Y., Liu, N., and Yu, P.S. (2025). Large Language Models for Robotics: A Survey. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Manzoor, S., Rocha, Y.G., Joo, S.H., Bae, S.H., Kim, E.J., Joo, K.J., and Kuc, T.Y. (2021). Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications. Appl. Sci., 11.","DOI":"10.3390\/app11104324"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1017\/S0373463319000638","article-title":"High Definition Map for Automated Driving: Overview and Analysis","volume":"73","author":"Liu","year":"2020","journal-title":"J. Navig."},{"key":"ref_57","first-page":"159","article-title":"OWL 2 web ontology language: Structural specification and functional-style syntax","volume":"27","author":"Motik","year":"2009","journal-title":"W3C Recomm."},{"key":"ref_58","unstructured":"Aeronautiques, C., Howe, A., Knoblock, C., McDermott, I.D., Ram, A., Veloso, M., Weld, D., Sri, D.W., Barrett, A., and Christianson, D. (1998). Pddl\u2014The Planning Domain Definition Language, Yale University. Technical Report."},{"key":"ref_59","unstructured":"Dong, J., Mukadam, M., Dellaert, F., and Boots, B. (2016, January 12\u201316). Motion planning as probabilistic inference using Gaussian processes and factor graphs. Proceedings of the Robotics: Science and Systems, Berkley, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carrera, A., Palomeras, N., Hurtos, N., and Carreras, M. (2015, January 7\u201311). Rosplan: Planning in the robot operating system. Proceedings of the International Conference on Automated Planning and Scheduling, Jerusalem, Israel.","DOI":"10.1609\/icaps.v25i1.13699"},{"key":"ref_61","unstructured":"Oleynikova, H., Millane, A., Taylor, Z., Galceran, E., Nieto, J., and Siegwart, R. (2016). Signed distance fields: A natural representation for both mapping and planning. Proceedings of the RSS 2016 Workshop: Geometry and Beyond-Representations, Physics, and SCENE understanding for Robotics, Ann Arbor, MI, USA, 28 June 2016, University of Michigan."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Coles, A., Coles, A., Fox, M., and Long, D. (2010, January 12\u201316). Forward-chaining partial-order planning. Proceedings of the International Conference on Automated Planning and Scheduling, Toronto, ON, Canada.","DOI":"10.1609\/icaps.v20i1.13403"},{"key":"ref_63","unstructured":"Dellaert, F. (2012). Factor Graphs and GTSAM: A Hands-on Introduction, Georgia Institute of Technology. Technical Report."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1729881419841532","DOI":"10.1177\/1729881419841532","article-title":"A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement","volume":"16","author":"Koide","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Huang, X., Cao, H., and Jia, B. (2023). Optimization of Levenberg Marquardt algorithm applied to nonlinear systems. Processes, 11.","DOI":"10.3390\/pr11061794"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1177\/0278364911430419","article-title":"iSAM2: Incremental smoothing and mapping using the Bayes tree","volume":"31","author":"Kaess","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hsu, D., Isler, V., Latombe, J.C., and Lin, M.C. (2011). The Bayes Tree: An Algorithmic Foundation for Probabilistic Robot Mapping. Algorithmic Foundations of Robotics IX: Selected Contributions of the Ninth International Workshop on the Algorithmic Foundations of Robotics, Springer.","DOI":"10.1007\/978-3-642-17452-0"},{"key":"ref_68","unstructured":"Kaplan, E.D., and Hegarty, C. (2017). Understanding GPS\/GNSS: Principles and applications, Artech House."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4271\/2016-01-0128","article-title":"Challenges in autonomous vehicle testing and validation","volume":"4","author":"Koopman","year":"2016","journal-title":"SAE Int. J. Transp. Saf."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.robot.2015.07.007","article-title":"Mobile robot localization via EKF and UKF: A comparison based on real data","volume":"74","author":"Lucia","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Geneva, P., Eckenhoff, K., Lee, W., Yang, Y., and Huang, G. (August, January 31). OpenVINS: A Research Platform for Visual-Inertial Estimation. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196524"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Sumikura, S., Shibuya, M., and Sakurada, K. (2019, January 21\u201325). OpenVSLAM: A Versatile Visual SLAM Framework. Proceedings of the 27th ACM International Conference on Multimedia, New York, NY, USA.","DOI":"10.1145\/3343031.3350539"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/MGRS.2020.2994107","article-title":"OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing","volume":"9","author":"Srivastava","year":"2021","journal-title":"IEEE Geosci. Remote Sens. 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